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Multiscale Processing Performance for Motion Capture

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Motion capture systems help record human motion as a sequence of joint angle vectors and analyse it in multiple degrees of freedom with high accuracy. Motion, as many other signals, might contain information which is stored on many different scales. Hence the use of a multiscale model might help correctly distinguish or analyse motion properties. In this paper we analyse the capabilities of a multiscale motion model to help distinguish meaningful motion features, whilst the unnecessary components (like noise) get removed. We performed experiments based on real motion capture data to analyse the discriminative properties of the multiscale approach. The main goal of experiments was to check the clustering performance of a multiscale model. The detailed results are presented and discussed, showing the capabilities and advantages of multiscale model application.
Rocznik
Strony
251--266
Opis fizyczny
Bibliogr. 16 poz., il., wykr.
Twórcy
autor
  • Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
autor
  • Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warszawa, Poland
Bibliografia
  • [1] Burt P. J., Adelson E. H.: The Laplacian Pyramid as a Compact Image Code. IEEE Trans. on Comunications, 31 (4), 532-542, 1983.
  • [2] Witkin A.: Scale-Space Filtering. IJCAI, 1019-1022, 1983.
  • [3] Shoemake K.: Animating Rotation with Quaternion Curves. Computer Graphics, 19 (3), 1985.
  • [4] Perona P., Malik J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. On Pattern Analysis and Machine Intelligence, 12 (7), 1990.
  • [5] Lindeberg T.: Scale-Space Theory in Computer Vision. Kluwer Academic Publishers, Netherlands, 1994.
  • [6] Johnson M. P.: Exploiting Quaternions to Support Expressive Interactive Character Motion. PhD thesis, Massachusetts Institute of Technology, 1995.
  • [7] Weickert J.: Anisotropic Diffusion in Image Processing. B.G. Teubner (1998).
  • [8] Lee J. and Shin S. Y.: A Coordinate-Invariant Approach to Multiresolution Motion Analysis, Graphical Models, 63 (2), 87-105, 2001.
  • [9] Lee J. and Shin S. Y., General Construction of Time-Domain Filters for Orientation Data. IEEE Transactions on Visualization and Computer Graphics, 8 (2), 119-128, 2002.
  • [10] Laptev I., Caputo B., Schuldt C. and Lindeberg T.: Local Velocity-Adapted Motion Events for Spatio-Temporal Recognition. Computer Vision and Image Understanding, 108, 207-229, 2007.
  • [11] Müller M., Röder T., Clausen M., Eberhardt B., Krüger B., and Weber A.: Documentation MOCAP Database HDM05. Universität Bonn, Bonn, Germany, Tech. Rep. CG-2007-2, Jun. 2007.
  • [12] Jablonski B., Kulbacki M.: Nonlinear Multiscale Analysis of Motion Trajectories. Lecture Notes in Computer Science, 6374, Springer, 122-130, 2010.
  • [13] Pradhan G. N., Prabhakaran B.: Clustering of human motions based on feature-level fusion of multiple body sensor data. Proceeding IHI 10 Proceedings of the 1st ACM International Health Informatics Symposium, ACM, 66-75, 2010.
  • [14] Jablonski B.: Application of Quaternion Scale Space Approach for Motion Processing. Image Processing and Communications Challenges 3, 102, Springer, 141-148, 2011.
  • [15] Jabloński B.: Quaternion Dynamic Time Warping. IEEE Trans. on Signal Processing, 60 (3), 1174- 1183, 2012.
  • [16] Zhou F., De la Torre F., Hodgins J. K.: Hierarchical Aligned Cluster Analysis for Temporal Segmentation of Human Motion. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2012 (to appear).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a6061b0e-a90d-431b-aa92-ab8b59f66c1b
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